arXiv AI July 14, 2026 By Prashant Kumar Choudhary, Nouhaila Innan, Muhammad Shafique, Rajeev Singh

MDQEC-QAS: Meta-Decoding for Quantum Error Correction with Hardware-Aware VQC Search and Confidence-Gated Recovery

arXiv:2607. 10707v1 Announce Type: cross Abstract: We propose a unified meta-decoding framework for quantum error correction that learns syndrome-to-recovery mappings across multiple stabilizer codes and noise settings, without requiring separate decoders for each configuration.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

benchmarks

arXiv Machine Learning

Jul 8

Latency-Constrained Hardware-Aware Quantum Error Correction Co-Design with Adaptive Confidence-Gated Neural Decoding for the Rotated Surface Code

arXiv:2607. 05814v1 Announce Type: cross Abstract: Real-time decoding is a major bottleneck in scaling quantum error correction (QEC) from noisy intermediate-scale quantum (NISQ) devices to fault-tolerant quantum computing.

By Sumit Chongder

efficiency benchmarks

arXiv Machine Learning

Jul 23

Machine-learned syndrome post-selection for reliable quantum error correction

arXiv:2607. 19563v1 Announce Type: cross Abstract: Quantum error correction can be enhanced by post-selecting out runs that are likely to produce a logical failure, but the most accurate measures for that require costly decoder-level information.

By Tobias Haug, Askery Canabarro, Leandro Aolita

efficiency

arXiv Machine Learning

Jul 31

QAdapt: A Noise-Adaptive Neural Pre-Decoding Framework for Quantum Error Correction

arXiv:2607. 28422v1 Announce Type: new Abstract: Fault-tolerant quantum computing (FTQC) relies on quantum error correction to suppress physical errors and preserve logical information at scale.

By Ran Miao, Rui Luo, Xiaohan Shan, Xiaoming Sun

fine-tuning benchmarks

arXiv Machine Learning

Jun 24

Quantum Adaptive Self-Attention for Quantum Transformer Models

arXiv:2504. 05336v4 Announce Type: replace-cross Abstract: A recurring weakness in quantum machine learning (QML) is that reported ``quantum advantages'' are seldom tested against a \emph{capacity-matched} classical control, leaving it unclear whether a gain comes from the quantum substrate or from the architectural change that accompanies it.

By Chi-Sheng Chen, En-Jui Kuo

llms benchmarks

arXiv Machine Learning

Jul 23

Statevector-Referenced Geometry Survival of a Four-Qubit ZZ Quantum Kernel on IBM Quantum Hardware: A Fixed-Subset Diagnostic Across Three Execution Configurations

arXiv:2607. 20377v1 Announce Type: cross Abstract: Quantum-kernel methods encode a dataset's geometry in a Gram matrix, so learning claims on hardware kernels assume the intended geometry survives execution.

By Rostyslav Sipakov

safety

arXiv AI

Aug 3

DreamQAS: Learning a Decision-Useful World Model for VQE-Efficient Quantum Architecture Search

arXiv:2607. 29491v1 Announce Type: cross Abstract: Reinforcement-learning-based quantum architecture search (RL-QAS) repeatedly optimizes a variational quantum eigensolver (VQE) after extending a circuit, although circuit construction and action legality are deterministic and known.

By Jiayang Niu, Yan Wang, Jie Li, Ke Deng, Azadeh Alavi, Muhammad Usman, Yongli Ren

reinforcement-learning

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Latency-Constrained Hardware-Aware Quantum Error Correction Co-Design with Adaptive Confidence-Gated Neural Decoding for the Rotated Surface Code

Machine-learned syndrome post-selection for reliable quantum error correction

QAdapt: A Noise-Adaptive Neural Pre-Decoding Framework for Quantum Error Correction

Quantum Adaptive Self-Attention for Quantum Transformer Models

Statevector-Referenced Geometry Survival of a Four-Qubit ZZ Quantum Kernel on IBM Quantum Hardware: A Fixed-Subset Diagnostic Across Three Execution Configurations

DreamQAS: Learning a Decision-Useful World Model for VQE-Efficient Quantum Architecture Search